






"""simple docstring"""







from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCamelCase	     =    logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCamelCase	     =    """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

def _a					(						_snake_case ,			_snake_case ,			_snake_case=8				):







      """simple docstring"""






      UpperCAmelCase							     =			height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      UpperCAmelCase							     =			width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class    lowerCamelCase__   (			_UpperCAmelCase					):






    def __init__(					self      ,A      ,A      ,A      ,):
          super().__init__()

          self.register_modules(
              unet=lowerCAmelCase__      ,scheduler=lowerCAmelCase__      ,movq=lowerCAmelCase__      ,)
          UpperCAmelCase							     =			2 ** (len(self.movq.config.block_out_channels   ) - 1)






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A      ,A      ,A      ,A   ):


          if latents is None:
                UpperCAmelCase							     =			randn_tensor(lowerCAmelCase__      ,generator=lowerCAmelCase__      ,device=lowerCAmelCase__      ,dtype=lowerCAmelCase__   )
          else:
                if latents.shape != shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''   )
                UpperCAmelCase							     =			latents.to(lowerCAmelCase__   )

          UpperCAmelCase							     =			latents * scheduler.init_noise_sigma
          return latents






    def 							_UpperCamelCase				(					self      ,A=0   ):


          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

          UpperCAmelCase							     =			torch.device(F'''cuda:{gpu_id}'''   )

          UpperCAmelCase							     =			[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(lowerCAmelCase__      ,lowerCAmelCase__   )






    def 							_UpperCamelCase				(					self      ,A=0   ):


          if is_accelerate_available() and is_accelerate_version(""">="""      ,"""0.17.0.dev0"""   ):
                from accelerate import cpu_offload_with_hook
          else:
                raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""   )

          UpperCAmelCase							     =			torch.device(F'''cuda:{gpu_id}'''   )

          if self.device.type != "cpu":
                self.to("""cpu"""      ,silence_dtype_warnings=lowerCAmelCase__   )
                torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          UpperCAmelCase							     =			None
          for cpu_offloaded_model in [self.unet, self.movq]:
                UpperCAmelCase     ,   UpperCAmelCase							     =			cpu_offload_with_hook(lowerCAmelCase__      ,lowerCAmelCase__      ,prev_module_hook=lowerCAmelCase__   )

          # We'll offload the last model manually.
          UpperCAmelCase							     =			hook






    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 							_UpperCamelCase				(					self   ):


          if not hasattr(self.unet      ,"""_hf_hook"""   ):
                return self.device
          for module in self.unet.modules():
                if (
                    hasattr(lowerCAmelCase__      ,"""_hf_hook"""   )
                    and hasattr(module._hf_hook      ,"""execution_device"""   )
                    and module._hf_hook.execution_device is not None
                ):
                      return torch.device(module._hf_hook.execution_device   )
          return self.device






    @torch.no_grad()
    @replace_example_docstring(lowerCAmelCase__   )
    def __call__(					self      ,A      ,A      ,A      ,A = 512      ,A = 512      ,A = 100      ,A = 4.0      ,A = 1      ,A = None      ,A = None      ,A = "pil"      ,A = True      ,):
          UpperCAmelCase							     =			self._execution_device

          UpperCAmelCase							     =			guidance_scale > 1.0

          if isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ):
                UpperCAmelCase							     =			torch.cat(lowerCAmelCase__      ,dim=0   )
          if isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ):
                UpperCAmelCase							     =			torch.cat(lowerCAmelCase__      ,dim=0   )
          if isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ):
                UpperCAmelCase							     =			torch.cat(lowerCAmelCase__      ,dim=0   )

          UpperCAmelCase							     =			image_embeds.shape[0] * num_images_per_prompt

          if do_classifier_free_guidance:
                UpperCAmelCase							     =			image_embeds.repeat_interleave(lowerCAmelCase__      ,dim=0   )
                UpperCAmelCase							     =			negative_image_embeds.repeat_interleave(lowerCAmelCase__      ,dim=0   )
                UpperCAmelCase							     =			hint.repeat_interleave(lowerCAmelCase__      ,dim=0   )

          UpperCAmelCase							     =			torch.cat([negative_image_embeds, image_embeds]      ,dim=0   ).to(dtype=self.unet.dtype      ,device=lowerCAmelCase__   )
          UpperCAmelCase							     =			torch.cat([hint, hint]      ,dim=0   ).to(dtype=self.unet.dtype      ,device=lowerCAmelCase__   )
          self.scheduler.set_timesteps(lowerCAmelCase__      ,device=lowerCAmelCase__   )
          UpperCAmelCase							     =			self.scheduler.timesteps

          UpperCAmelCase							     =			self.movq.config.latent_channels

          UpperCAmelCase     ,   UpperCAmelCase							     =			downscale_height_and_width(lowerCAmelCase__      ,lowerCAmelCase__      ,self.movq_scale_factor   )

          # create initial latent
          UpperCAmelCase							     =			self.prepare_latents(
              (batch_size, num_channels_latents, height, width)      ,image_embeds.dtype      ,lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__      ,self.scheduler      ,)

          for i, t in enumerate(self.progress_bar(lowerCAmelCase__   )   ):
                # expand the latents if we are doing classifier free guidance
                UpperCAmelCase							     =			torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

                UpperCAmelCase							     =			{"""image_embeds""": image_embeds, """hint""": hint}
                UpperCAmelCase							     =			self.unet(
                    sample=lowerCAmelCase__      ,timestep=lowerCAmelCase__      ,encoder_hidden_states=lowerCAmelCase__      ,added_cond_kwargs=lowerCAmelCase__      ,return_dict=lowerCAmelCase__      ,)[0]

                if do_classifier_free_guidance:
                      UpperCAmelCase     ,   UpperCAmelCase							     =			noise_pred.split(latents.shape[1]      ,dim=1   )
                      UpperCAmelCase     ,   UpperCAmelCase							     =			noise_pred.chunk(2   )
                      UpperCAmelCase     ,   UpperCAmelCase							     =			variance_pred.chunk(2   )
                      UpperCAmelCase							     =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      UpperCAmelCase							     =			torch.cat([noise_pred, variance_pred_text]      ,dim=1   )

                if not (
                    hasattr(self.scheduler.config      ,"""variance_type"""   )
                    and self.scheduler.config.variance_type in ["learned", "learned_range"]
                ):
                      UpperCAmelCase     ,   UpperCAmelCase							     =			noise_pred.split(latents.shape[1]      ,dim=1   )

                # compute the previous noisy sample x_t -> x_t-1
                UpperCAmelCase							     =			self.scheduler.step(
                    lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__      ,generator=lowerCAmelCase__      ,)[0]
          # post-processing
          UpperCAmelCase							     =			self.movq.decode(lowerCAmelCase__      ,force_not_quantize=lowerCAmelCase__   )["""sample"""]

          if output_type not in ["pt", "np", "pil"]:
                raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''   )

          if output_type in ["np", "pil"]:
                UpperCAmelCase							     =			image * 0.5 + 0.5
                UpperCAmelCase							     =			image.clamp(0      ,1   )
                UpperCAmelCase							     =			image.cpu().permute(0      ,2      ,3      ,1   ).float().numpy()

          if output_type == "pil":
                UpperCAmelCase							     =			self.numpy_to_pil(lowerCAmelCase__   )

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=lowerCAmelCase__   )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)